Dechun Zhao1, Renpin Jiang2, Mingyang Feng1, Jiaxin Yang1, Yi Wang1, Xiaorong Hou3, Xing Wang4. 1. College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China. 2. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China. 3. College of Medical Informatics, Chongqing Medical University, Chongqing, China. 4. College of Bioengineering, Chongqing University, Chongqing, China.
Abstract
BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection. OBJECTIVE: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory. METHODS: The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 ∼ N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result. RESULTS: The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%. CONCLUSION: These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction.
BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection. OBJECTIVE: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory. METHODS: The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 ∼ N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result. RESULTS: The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%. CONCLUSION: These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction.
Entities:
Keywords:
Sleep staging; deep learning; long short-term memory; one-dimensional convolutional neural network
Authors: Huy Phan; Fernando Andreotti; Navin Cooray; Oliver Y Chen; Maarten De Vos Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2019-01-31 Impact factor: 3.802